The typical communications transmitter comprises a signal generator, for example, a processor, for providing a low power input signal to be transmitted. The transmitter may also include a power amplifier coupled to the signal generator, and an antenna coupled to an output of the power amplifier. The power amplifier amplifies the power of the input signal from the signal generator for transmission via the antenna.
In an ideal implementation, the power amplifier is a linear device, i.e. the power amplifier produces an amplified replica of the low power electrical signal. In other words, the amplified signal may have identical phase and frequency characteristics to the input signal. Nonetheless, the typical power amplifier may be nonlinear. The power amplifier produces an amplified signal that may have distinctive phase and frequency characteristics to the input signal, i.e. the amplified signal is distorted.
The typical power amplifier may be near linear within an optimum operating range, for example, a certain range of amplitude in the input signal. Outside this optimum operating range, the power amplifier may become nonlinear. Indeed, as the input signal deviates further from the optimum operating range of the power amplifier, the nonlinearity of the power amplifier may increase. The typical optimum operating range of the power amplifier may be increased with a corresponding increase in the size and complexity of the power amplifier. This increase in size and power consumption may be undesirable for low power communications, for example, satellite relay communications.
An approach to address the shortcomings of a nonlinear power amplifier may be to include a predistortion filter coupled between the signal generator and the power amplifier. The predistortion filter predistorts the input signal to have inverse changes, such as, in the frequency and phase characteristics, to those imparted by the distortion of the power amplifier so that the amplified signal may have a greater degree of linearity.
Another approach to the predistortion filter is disclosed in the article Kim et al., “Digital Predistortion of Wideband Signals Based On Power Amplifier Model With Memory,” Electronics Letters, pages 1417-1418, 8 Nov. 2001, Vol. 37, Issue 23, the entire contents of which are incorporated here by reference. This method includes using a memory polynomial to predistort the input signal.
Another approach to the predistortion filter is disclosed in the article Ding et al., “A Memory Polynomial Predistorter Implemented USING TMS320C67XX,” the entire contents of which are incorporated here by reference. The device of Ding et al. includes a memory polynomial predistortion filter, and a predistortion construction module for generating the memory polynomial for predistorting the input signal. The memory polynomial of Ding et al. has the formula below, where x(n) is the input signal, z(n) is the predistorted input signal, K is the order of nonlinearity, Q is the amount of memory, and akq are complex valued coefficients.
      z    ⁡          (      n      )        =            ∑              k        =        1            K        ⁢                  ∑                  q          =          0                Q            ⁢                        a          kq                ⁢                  x          ⁡                      (                          n              -              q                        )                          ⁢                                                        x              ⁡                              (                                  n                  -                  q                                )                                                                      k            -            1                              
Another approach to the predistortion filter is disclosed in U.S. Patent Application Publication No. 2007/0063770 to Rexberg. The predistortion filter of Rexberg includes a Finite Impulse Response (FIR) structure and corresponding lookup tables. Rexberg also uses the memory polynomial of Ding et al. but expands the Q term in the memory polynomial for computation to provide the following result.
                    Separate                            delays                            into                            different                            sums              ⁢      {                                        =                                                            ∑                                      k                    =                    1                                    K                                ⁢                                                      a                                          k                      ⁢                                                                                          ⁢                      0                                                        ⁢                                      x                    ⁡                                          (                      n                      )                                                        ⁢                                                                                                          x                        ⁡                                                  (                          n                          )                                                                                                                                  k                      -                      1                                                                                  +                                                                                      +                                                ∑                                      k                    =                    1                                    K                                ⁢                                                      a                                          k                      ⁢                                                                                          ⁢                      1                                                        ⁢                                      x                    ⁡                                          (                                              n                        -                        1                                            )                                                        ⁢                                                                                                          x                        ⁡                                                  (                                                      n                            -                            1                                                    )                                                                                                                                  k                      -                      1                                                                                            +                                                ⋮                                                  +                                          ∑                                  k                  =                  1                                K                            ⁢                                                a                  kQ                                ⁢                                  x                  ⁡                                      (                                          n                      -                      Q                                        )                                                  ⁢                                                                                                x                      ⁡                                              (                                                  n                          -                          Q                                                )                                                                                                                      k                    -                    1                                                                                          
Nonetheless, the approach of Rexberg may suffer from several drawbacks. Specifically, the expansion of the memory polynomial in Rexberg may not be easily reduced to a FIR filter structure without making several assumptions that may reduce the effectiveness of the predistortion filter. Moreover, the remaining summation terms may not be reduced to respective FIR filters.
In these approaches to the predistortion filter that uses memory polynomials, the device may include a processor for producing the memory polynomial coefficients. This computation of coefficients may be intensive and can become cumbersome when applied to a high bandwidth input signal.